Figure - available from: Electrical Engineering
This content is subject to copyright. Terms and conditions apply.
Source publication
Accurate forecast of monthly electricity consumption has guiding significance for the economic dispatch of the power system, and it is also a prerequisite for the power company to formulate a reasonable sales plan. The traditional forecasting method of monthly electricity consumption performs poorly in processing the sequence of monthly electricity...
Similar publications
Background:
Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor r...
Citations
... A random forest prediction method of monthly electricity consumption based on the maximum mutual information coe cient was done using python in order to have model that will predict consumption. [9] The objective of this study is to establish a comprehensive framework for power systems organization that can be utilized in production and consumption planning. Additionally, the study aims to evaluate and implement various forecasting methods to leverage the unique advantages of each principle.Until now the decision that needed to be made concerning which sources would work in which period of time so the power could safely be delivered to consumers were left to the human eye. ...
This paper presents artificial neural network (ANN) models for prediction of energy consumption compared with autoregressive integrated moving average (ARIMA) modeland adaptive neuro fuzzy inference system (ANFIS) models for thermal power plant production, hydro power plant production as well as CO 2 emission.These models were developed to streamline power systems planning and minimize errors in planning. The model's accurate functionality is ensured by incorporating the collective expertise of power system planning professionals accumulated over the years. By adhering to these calculations, human errors are eliminated, the benefits of human knowledge are maximized, and a precise framework is established for planning the functionality of large power systems. ANN model for prediction were compared with autoregressive integrated moving average (ARIMA) model. Prediction of power plants production is an effective way to organize power systems for future time intervals so there will be less problems in the growing complexity of power systems. These are problems with a great number of data whose connections are not easy to define. In this paper the solution is presented in the form of ANNas part of supervised machine learning (SML) and ANFISmethodology. Calculations with ANN model was done simultaneously with standard forecasting ARIMA model in order to facilitate a comparison. Databases that are used for training ANN, ANFIS and ARIMA models contain hours of yearly data from Serbian power systems that are related to production of different energy sources, energy consumption and parameters that influence them. Trained ANN models are used to predict energy consumption and ANFIS models are used for prediction of energy production from different energy sources and CO 2 emission on yearly or daily basis. Considering the existence of great numbers of data from previous years from which experts made conclusions for production scheduling this data is used to train ANFIS models that will help experts in optimal production scheduling. Using models like these human errors would be minimized and planning will be optimized and improved for experts.
... Instead of relying solely on one decision tree, the random forest gathers the results from each tree and anticipates the last outcome according to most forecasts. 18 Random forest calculation disposes of overfitting as the outcome depends on a more significant part of the vote or average. Every decision tree shape is free of the others, showing the parallelization property. ...
Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia.
... Machine learning methods generally include XGBoost , random forest [21], fuzzy logic [22], support vector machine (SVM) [23], artificial neural network (ANN) [24] and deep neural network (deep neural network) network, DNN) [25] and other datadriven machine learning methods [26] and so on. Literature [27] proposes an electric vehicle charging load prediction model based on the fusion of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM); Literature [28] uses the Prophet algorithm The field of load forecasting was introduced and combined with the XGBoost algorithm to improve the accuracy of Prophet load forecasting; the literature [29] proposed a power system load forecasting method that combines fuzzy clustering and random forest regression algorithms, using rough sets to construct compensation rules , to correct and compensate the prediction results; the literature [30] combines high correlation factor data and uses the random forest method to predict monthly electricity consumption. Literature [31] proposed a method based on empirical wavelet transform and random forest to solve the problem of strong randomness and low prediction accuracy of short-term electric load. ...
Oil and gas fields have a large amount of distributed new energy. In order to improve the utilization rate of new energy and respond to the dispatching needs of China's State Grid, it is necessary to study the use of ultra-short-term load forecasting algorithms to improve the load forecasting accuracy of oil and gas fields and support the coordinated interaction of source, grid and load in the integrated energy system of oil and gas fields. This paper proposes an ultra-short-term load forecasting algorithm based on a hybrid neural network called Convolutional-Bidirectional Long Short Term Memory-Skip (CNN-BiLSTM-Skip). Using the operating load data of an oil and gas field in Northeast China as a data set, we first constructed a cooling, heating and power system architecture model with wind turbines, photovoltaics, power grids and natural gas as “source and grid loads”; Secondly, we used an improved hybrid multi-time scale algorithm and unit A prediction model was constructed based on the operating load data, and the prediction results of the nonlinear part and linear part of the model were output and integrated to obtain the final prediction result; Finally, the prediction error evaluation index of the algorithm proposed in this article was compared with algorithms such as BP, LSTM, and CNN-LSTM. The results show that the algorithm proposed in this article has stronger robustness and higher accuracy. The proposed CNN-BiLSTM-SKIP algorithm improves the prediction accuracy. Compared with the BP neural network algorithm, the MAPE evaluation index has an average accuracy increase of 3.78%, compared with the LSTM prediction algorithm, the accuracy has increased by 1.63% on average, and compared with the CNN-LSTM prediction algorithm, the accuracy has increased by 0.74% on average; and the proposed prediction algorithm is compared with the BP neural network algorithm, LSTM prediction algorithm and CNN-LSTM algorithm, the RMSE and MAE evaluation index values are both the smallest, which can support the collaborative interaction of oil and gas field source, network and load and realize the planning and dispatching needs.
... It is a promising method for estimating the E of rock. Recently, the XGBoost and RF algorithms have shown great potential to improve prediction accuracy and have been successfully applied in many fields, such as electricity consumption forecasting [39,40], infectious disease prediction [41,42], mining maximum subsidence prediction [43,44], and heavy metal contamination prediction [45,46]. XGBoost and RF are two efficient ensemble methods that combine multiple homogeneous weak learners in certain ways to reduce overfitting. ...
Accurate estimation of the elastic modulus (E) of rock is critical for the design of geotechnical projects such as mining, slopes, and tunnels. However, the determination of rock mechanical parameters usually involves high budget and time requirements. To address this problem, numerous researchers have developed machine learning models to estimate the E of rock. In this study, two novel hybrid ensemble learning models were developed to estimate the E of rock by optimizing the extreme gradient boosting (XGBoost) and random forest (RF) algorithms through the dwarf mongoose optimization (DMO) approach. Firstly, 90 rock samples with porosity, dry density, P-wave velocity, slake durability, and water absorption as input indicators were collected. Subsequently, the hyperparameters of XGBoost and RF were tuned by DMO. Based on the optimal hyperparameters configuration, two novel hybrid ensemble learning models were constructed using the training set (80% of the data). Finally, the performance of the developed models was evaluated by the coefficient of determination (R2 score), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF) on the test set (20% of the data). The results show that the DMO-RF model achieved the best comprehensive performance with an R2 score of 0.967, RMSE of 0.541, MAE of 0.447, and VAF of 0.969 on the test set. The dry density and slake durability were more influential indicators than others. Moreover, the convergence curves suggested that the DMO-RF model can reduce the generalization error and avoid overfitting. The developed models can be regarded as viable and useful tools in estimating the E of rock.
... A home in Portugal features a Home Energy Management System (HEMS), batteries, and solar panels. The value reported in [38] for the maximum mutual knowledge quantum for monthly power use. The elements with the highest reciprocal information coefficient have been selected. ...
... It leads to a flawed strategy for the smart grid's electricity forecasting. • The energy forecasting methods in the research articles [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] utilized conventional prediction models such as ANN, SVM, etc. These classifiers, however, experience an incorrect training phase, become trapped in local optima, and converge slowly to the best answers. ...
... In this section, researchers have contrasted the proposed model CAC-OWOA with current energy consumption forecasting models that employ various approaches. In comparison to several modern methods created by authors in article [35], [36], [37], and [38], researchers assessed the performance of the suggested CAC-OWOA model. The creators of these methods have used DA and GA in [35], artificial bee optimization in [36], ensemble prediction model in [37], and random forest method in [38] to predict power usage, which makes them similar to the proposed methodology. ...
For customers to participate in key peak pricing, period-of-use fees, and individualized responsiveness to demand programmes taken from multi-dimensional data flows, energy use projection and analysis must be done well. However, it is a difficult study topic to ascertain the knowledge of use of electricity as recorded in the electricity records' Multi-Dimensional Data Streams (MDDS). Context-Aware Clustering (CAC) and the Optimized Whale Optimization Algorithm were suggested by researchers as a fresh power usage knowledge finding model from the multi-dimensional data streams (MDDS) to resolve issue (OWOA). The proposed CAC-OWOA framework first performs the data cleaning to handle the noisy and null elements. The predictive features are extracted from the novel context-aware group formation algorithm using the statistical context parameters from the pre-processed MDDS electricity logs. To perform the energy consumption prediction, researchers have proposed the novel Artificial Neural Network (ANN) predictive algorithm using the bio-inspired optimization algorithm called OWOA. The OWOA is the modified algorithm of the existing WOA to overcome the problems of slow convergence speed and easily falling into the local optimal solutions. The ANN training method is used in conjunction with the suggested bio-inspired OWOA algorithm to lower error rates and boost overall prediction accuracy. The efficiency of the CAC-OWOA framework is evaluated using the publicly available smart grid electricity consumption logs. The experimental results demonstrate the effectiveness of the CAC-OWOA framework in terms of forecasting accuracy, precision, recall, and duration when compared to underlying approaches.
... The instance was a house in Portugal with solar panels and batteries and a home energy management system (HEMS) in charge. The largest reciprocal knowledge coefficient for monthly electricity use had proposed in [33]. First, the highest reciprocal knowledge coefficient had established between monthly power usage and its affecting elements. ...
... • The machine learning techniques such as ANN, SVM, KNN, random forest, etc. were utilized for the prediction either individually or combined with clustering [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. These techniques already suffered from Mean Square Error (MSE), training error, and accuracy challenges. ...
... al [31], Bot et. al [32], and Pang et.al [33]. These methods are closely related to the proposed model where they have used Dragonfly and genetic algorithm in [30], Artificial bee optimization in [31], ensemble forecasting model in [32], and random forest model in [33] for energy consumption prediction. ...
Energy consumption forecasting is a hot field of research; despite the number of developed models, projecting electric consumption in residential buildings remains problematic owing to the significant unpredictability of occupant energy use behavior. Discovering the electricity consumption knowledge from the multi-dimensional data streams (MDDS) of electricity logs is a challenging research problem. We propose a novel electricity knowledge discovery model proposed from the MDDS using clustering and machine learning. Context-aware clustering with whale optimization algorithm (CAC-WOA) is proposed to discover the predictive features from the electricity MDDS and perform the predictions using WOA. The CAC-WOA consists of two phases context-aware group formation and a WOA-based machine learning predictive model. In the CAC algorithm, group formation using electricity contextual information to estimate the robust predictive features are proposed. Using such predictive features, the predictive model using the WOA-based artificial neural network (ANN) is built. The modified ANN technique using the WOA algorithm is used to reduce the error rates and improve the prediction accuracy. The experimental outcomes using publicly available electricity consumption datasets prove the efficiency of the CAC-WOA model. Overall prediction accuracy is improved by 3.27% and prediction time is reduced by 11.31% using CAC-WOA compared state-of-the-art solutions.
... The instance was a house in Portugal with solar panels and batteries and a Home Energy Management System (HEMS) in charge. The largest reciprocal knowledge coefficient for monthly electricity use had proposed in [33]. First, the highest reciprocal knowledge coefficient had established between monthly power usage and its affecting elements. ...
... • The machine learning techniques such as ANN, SVM, KNN, random forest, etc. were utilized for the prediction either individually or combined with clustering [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. These techniques already suffered from Mean Square Error (MSE), training error, and accuracy challenges. ...
... The performance of the proposed CAC group formation algorithm has been enhanced by applying the optimized predictive model called WOA-ANN (i.e., CAC-WOA). [32], and Pang et.al [33]. These methods are closely related to the proposed model where they have used Dragonfly and genetic algorithm in [30], Artificial bee optimization in [31], ensemble forecasting model in [32], and random forest model in [33] for energy consumption prediction. ...
Due to the widespread use of smart metering infrastructure, multidimensional data on home electric consumption is easily available for studying its dynamics at finely resolved geographical and temporal scales. Effective forecasting and analysis of electric consumption are crucial for customer participation in time-of-use tariffs, critical peak pricing, and user-specific demand response programs derived from multidimensional data streams. Along with the enormous economic and sustainability ramifications, such as energy waste and the decarbonisation of the energy industry, precise consumption forecasts enable power system planning and reliable grid operations. Energy consumption forecasting is a hot field of research; despite the number of developed models, projecting electric consumption in residential buildings remains problematic owing to the significant unpredictability of occupant energy use behaviour. Discovering the electricity consumption knowledge from the Multi-Dimensional Data Streams (MDDS) of electricity logs is a challenging research problem. To end this, a novel electricity knowledge discovery model proposed from the MDDS using clustering and machine learning. Context-Aware Clustering with Whale Optimization Algorithm (CAC-WOA) is designed and explained in this research article. The CAC-WOA consists of two phases context-aware groups formation and WOA-based machine learning predictive model. In the CAC algorithm, group's formation using electricity contextual information to estimate the robust predictive features are proposed. Using such predictive features, the predictive model using the WOA-based Artificial Neural Network (ANN) is built. The modified ANN technique using the WOA algorithm is used to reduce the error rates and improve the prediction accuracy. The experimental outcomes using publically available electricity consumption datasets prove the efficiency of the CAC-WOA model.
